A fuzzy inductive learning algorithm for parallel loop scheduling

Chang-Jiun Tsai Chang-Jiun Tsai,Chao-Tung Yang Chao-Tung Yang,Shain-Shyong Tseng Shain-Shyong Tseng,Ching-Hung Wang Ching-Hung Wang,Mon-Fong Jiang Mon-Fong Jiang

doi:10.1109/icsmc.1997.625745

Chang-Jiun Tsai Chang-Jiun Tsai, Chao-Tung Yang Chao-Tung Yang + Show 3 more

https://doi.org/10.1109/icsmc.1997.625745

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The conventional symbolic learning algorithm can not infer data that contains fuzzy information. In the past few years, we have designed a parallel loop scheduling method called KPLS based upon a knowledge based approach, that chooses an appropriate schedule for a different loop to assign loop iterations to a multiprocessor system for achieving high speedup rates. Unfortunately, we found that the attributes that were applied in KPLS contain some fuzzy information, which are inapplicable to the traditional symbolic learning strategy for inferring some concept descriptions. In this paper, we apply a fuzzy set concept to an AQR learning algorithm that is called FAQR. FAQR which can induce fuzzy linguistic rules from fuzzy instances is then proposed to solve the above parallel loop scheduling problem. Some promising inference rules have been found and applied to infer the choice of parallel loop scheduling. We apply FAQR to the IRIS flower classification problem. Experimental results show that our method yields high accuracy in both domains.

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